最佳控制理论神经优化器:从反向传播到动态编程
IEEE transactions on pattern analysis and machine intelligence
|November 19, 2025
概括
这项研究将深度神经网络 (DNN) 优化与最佳控制理论联系起来. 一个新的优化器,OCNOpt,利用动态编程来实现更强大,更有效的DNN训练.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 控制理论 控制理论
背景情况:
- 深度神经网络 (DNN) 对人工智能进步至关重要,但它们的优化是复杂的.
- 可以将DNN视为动态系统,通过最佳控制实现分析.
- 现有的优化方法,如反向传播,与动态编程具有算法相似之处.
研究的目的:
- 探索反向传播和动态编程之间的算法联系.
- 开发一种基于高阶贝尔曼方程扩展的新类DNN优化方法.
- 为了介绍最佳控制理论神经优化器 (OCNOpt).
主要方法:
- 将DNN解释为最佳控制编程中的动态系统.
- 利用反向传播的变化结构.
- 为优化应用贝尔曼方程的高阶扩展.
- 开发一个OCNOpt算法.
主要成果:
- 与现有方法相比,OCNOpt表现出更好的稳定性和效率.
- 优化器保持可管理的计算复杂性.
- OCNOpt能够实现新的应用程序,如层级反和游戏理论方法.
结论:
- 反向传播和动态编程之间的联系为DNN优化提供了新的视角.
- OCNOpt提供了基于最佳控制理论的原则算法方法.
- 这项工作为设计高效和强大的DNN开辟了新的途径.
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